A Survey on Prompt Tuning

• URL: http://arxiv.org/abs/2507.06085v2
• Date: Wed, 09 Jul 2025 09:59:12 GMT
• Title: A Survey on Prompt Tuning
• Authors: Zongqian Li, Yixuan Su, Nigel Collier,
• Abstract summary: We classify existing approaches into two categories: direct prompt learning and transfer learning.<n>For each method, we analyze method designs, innovations, insights, advantages, and disadvantages.<n>We discuss future directions in improving training robustness and broadening application scope.
• Score: 32.4489985319054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This survey reviews prompt tuning, a parameter-efficient approach for adapting language models by prepending trainable continuous vectors while keeping the model frozen. We classify existing approaches into two categories: direct prompt learning and transfer learning. Direct prompt learning methods include: general optimization approaches, encoder-based methods, decomposition strategies, and mixture-of-experts frameworks. Transfer learning methods consist of: general transfer approaches, encoder-based methods, and decomposition strategies. For each method, we analyze method designs, innovations, insights, advantages, and disadvantages, with illustrative visualizations comparing different frameworks. We identify challenges in computational efficiency and training stability, and discuss future directions in improving training robustness and broadening application scope.

Related papers

• Learning Regularization Functionals for Inverse Problems: A Comparative Study [57.289041896491206]
  A variety of learned regularization frameworks for solving inverse problems in imaging have emerged.<n>These offer flexible modeling together with mathematical insights.<n>We address this gap by collecting and unifying the available code into a common framework.
  arXiv  Detail & Related papers  (2025-10-02T07:42:28Z)
• Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why [50.191655141020505]
  This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations.<n>We argue that the dichotomy between feature-based and GAN-based methods is increasingly nuanced.
  arXiv  Detail & Related papers  (2025-07-08T11:45:51Z)
• Towards Differentiable Multilevel Optimization: A Gradient-Based Approach [1.6114012813668932]
  This paper introduces a novel gradient-based approach for multilevel optimization. Our method significantly reduces computational complexity while improving both solution accuracy and convergence speed. To the best of our knowledge, this is one of the first algorithms to provide a general version of implicit differentiation.
  arXiv  Detail & Related papers  (2024-10-15T06:17:59Z)
• Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement [1.1510009152620668]
  Current methods for constructive neural optimization usually train a policy using behavior cloning from expert solutions or policy gradient methods from reinforcement learning. We bridge the two by sampling multiple solutions for random instances using the current model in each epoch and then selecting the best solution as an expert trajectory for supervised imitation learning. We evaluate our approach on the Traveling Salesman Problem and the Capacitated Vehicle Routing Problem. The models trained with our method achieve comparable performance and generalization to those trained with expert data.
  arXiv  Detail & Related papers  (2024-03-22T13:09:10Z)
• Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach [0.3093890460224435]
  We address the solution of the popular Wordle puzzle, using new reinforcement learning methods. For the Wordle puzzle, they yield on-line solution strategies that are very close to optimal at relatively modest computational cost.
  arXiv  Detail & Related papers  (2022-11-15T03:46:41Z)
• Demystifying Unsupervised Semantic Correspondence Estimation [13.060538447838303]
  We explore semantic correspondence estimation through the lens of unsupervised learning. We thoroughly evaluate several recently proposed unsupervised methods across multiple challenging datasets. We introduce a new unsupervised correspondence approach which utilizes the strength of pre-trained features while encouraging better matches during training.
  arXiv  Detail & Related papers  (2022-07-11T17:59:51Z)
• Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [101.32332941117271]
  Decision making algorithms are used in a multitude of different applications. Deep learning approaches that use highly parametric architectures tuned from data without relying on mathematical models are becoming increasingly popular. Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.
  arXiv  Detail & Related papers  (2022-05-05T13:40:08Z)
• Towards a Unified View of Parameter-Efficient Transfer Learning [108.94786930869473]
  Fine-tuning large pre-trained language models on downstream tasks has become the de-facto learning paradigm in NLP. Recent work has proposed a variety of parameter-efficient transfer learning methods that only fine-tune a small number of (extra) parameters to attain strong performance. We break down the design of state-of-the-art parameter-efficient transfer learning methods and present a unified framework that establishes connections between them.
  arXiv  Detail & Related papers  (2021-10-08T20:22:26Z)
• A Survey on Deep Semi-supervised Learning [51.26862262550445]
  We first present a taxonomy for deep semi-supervised learning that categorizes existing methods. We then offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences.
  arXiv  Detail & Related papers  (2021-02-28T16:22:58Z)
• Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting [41.94295877935867]
  We present application-driven learning, a new closed-loop framework in which the processes of forecasting and decision-making are merged and co-optimized. We show that the proposed methodology is scalable and yields consistently better performance than the standard open-loop approach.
  arXiv  Detail & Related papers  (2021-02-26T02:43:28Z)
• There and Back Again: Revisiting Backpropagation Saliency Methods [87.40330595283969]
  Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a signal and analyzing the resulting gradient. We propose a single framework under which several such methods can be unified.
  arXiv  Detail & Related papers  (2020-04-06T17:58:08Z)
• Disentangling Adaptive Gradient Methods from Learning Rates [65.0397050979662]
  We take a deeper look at how adaptive gradient methods interact with the learning rate schedule. We introduce a "grafting" experiment which decouples an update's magnitude from its direction. We present some empirical and theoretical retrospectives on the generalization of adaptive gradient methods.
  arXiv  Detail & Related papers  (2020-02-26T21:42:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.